This invention relates generally to the field of geophysical prospecting and, more particularly, to seismic data interpretation. Specifically, the invention is a method for analyzing the morphology of seismic objects extracted from a three-dimensional (3D) seismic data volume.
In the oil and gas industry, seismic prospecting techniques commonly are used to aid in the search for and evaluation of subterranean hydrocarbon deposits. A seismic prospecting operation consists of three separate stages: data acquisition, data processing, and data interpretation, and success of the operation depends on satisfactory completion of all three stages.
In the data acquisition stage, a seismic source is used to generate an acoustic impulse known as a xe2x80x9cseismic signalxe2x80x9d that propagates into the earth and is at least partially reflected by subsurface seismic reflectors (i.e., interfaces between underground formations having different acoustic impedances). The reflected signals (known as xe2x80x9cseismic reflectionsxe2x80x9d) are detected and recorded by an array of seismic receivers located at or near the surface of the earth, in an overlying body of water, or at known depths in boreholes. The seismic energy recorded by each seismic receiver is known as a xe2x80x9cseismic data trace.xe2x80x9d
During the data processing stage, the raw seismic data traces recorded in the data acquisition stage are refined and enhanced using a variety of procedures that depend on the nature of the geologic structure being investigated and on the characteristics of the raw data traces themselves. In general, the purpose of the data processing stage is to produce an image of the subsurface from the recorded seismic data for use during the data interpretation stage. The image is developed using theoretical and empirical models of the manner in which the seismic signals are transmitted into the earth, attenuated by subsurface strata, and reflected from geologic structures. The quality of the final product of the data processing stage is heavily dependent on the accuracy of the procedures used to process the data.
The purpose of the data interpretation stage is to determine information about the subsurface geology of the earth from the processed seismic data. The results of the data interpretation stage may be used to determine the general geologic structure of a subsurface region, or to locate potential hydrocarbon reservoirs, or to guide the development of an already discovered reservoir.
Currently, 3D seismic data is the preferred tool for subsurface exploration. As used herein, a xe2x80x9c3D seismic data volumexe2x80x9d is a 3D volume of discrete x-y-z or x-y-t data points, where x and y are mutually orthogonal, horizontal directions, z is the vertical direction, and t is two-way vertical seismic signal traveltime. These discrete data points are often represented by a set of contiguous hexahedrons known as xe2x80x9ccellsxe2x80x9d or xe2x80x9cvoxels,xe2x80x9d with each cell or voxel representing the volume surrounding a single data point. Each cell or voxel typically has an assigned value of a specific seismic attribute such as seismic amplitude, seismic impedance, or any other seismic data attribute that can be defined on a point-by-point basis.
A common problem in interpretation of a 3D seismic data volume is the extraction of xe2x80x9cseismic objectsxe2x80x9d from the data volume and evaluation of their geometric relationships to each other. A xe2x80x9cseismic objectxe2x80x9d is defined as a region of the 3D seismic data volume in which the value of a certain selected seismic attribute (e.g., acoustic impedance) satisfies some arbitrary threshold requirement, i.e., is either greater than some minimum value or less than some maximum value. At a certain threshold, two such regions may not be connected (i.e., they are two separate seismic objects); at a different threshold, they may be connected (i.e., a single seismic object). The interpreter must decide which threshold depicts a scenario that is more consistent with other known information about the subterranean region in question. Selection of an appropriate threshold is not always straightforward, and it may take multiple iterations to achieve the desired result which, of course, is that the seismic objects should correspond to actual underground reservoirs.
One technique for identifying and extracting seismic objects from a 3D seismic data volume is known as xe2x80x9cseed picking.xe2x80x9d Seed picking results in a set of voxels in a 3D seismic data volume, which fulfill user-specified attribute criteria and are connected. Seed picking has been implemented in several commercial software products such as VoxelGeo(copyright), VoxelView(copyright), GeoViz(copyright), Gocad(copyright), and others. Seed picking is an interactive method, where the user specifies the initial xe2x80x9cseedxe2x80x9d voxel and attribute criteria. The seed picking algorithm marks an initial voxel as belonging to the current object, and tries to find neighbors of the initial voxel that satisfy the specified attribute criteria. The new voxels are added to the current object, and the procedure continues until it is not possible to find any new neighbors fulfilling the specified criteria.
Seed picking requires a criterion for connectedness. There are two criteria commonly used, although others may be defined and used. One definition is that two voxels are connected (i.e., are neighbors) if they share a common face. By this definition of connectivity, a voxel can have up to six neighbors. The other common criterion for being a neighbor is sharing either an edge, a face, or a comer. By this criterion, a voxel can have up to twenty-six neighbors.
Another technique for identifying and extracting seismic objects from a 3D seismic data volume is by identifying discontinuities in the data using trace-to-trace correlations. These discontinuities may be assumed to represent the boundaries between contiguous seismic objects.
Current techniques for extracting seismic objects from 3D seismic data volumes fail to capture the valuable information about subsurface stratigraphy that is represented by the morphology of the extracted seismic objects. There is clearly a need for a method for capturing this information. Such a method preferably should be capable of analyzing a wide range of morphologic parameters and of operating automatically based on user-specified input conditions. The present invention satisfies this need.
In a first embodiment, the invention comprises a method for analyzing and classifying the morphology of a seismic object extracted from a 3D seismic data volume comprising the steps of (a) selecting one or more morphologic parameters for use in classifying the morphology of the seismic object, (b) performing geometric analyses of the seismic object to determine geometric statistics corresponding to the morphologic parameters, and (c) using the geometric statistics to classify the morphology of the seismic object according to the morphologic parameters. The seismic object may comprise any type of seismic data, including but not limited to seismic amplitude data, seismic impedance data, and seismic attribute data.
In another embodiment, the invention includes the steps of fitting one or more surfaces to the seismic object and performing geometric analyses of these surfaces. Typically, these surfaces would comprise surfaces conforming to the top, middle, or base of the seismic object.
In another embodiment, the invention also includes the step of extracting one or more seismic objects from the original 3D seismic data volume. The seismic object(s) may be extracted using any known technique. For example, the seismic object(s) may be extracted using multi-threshold, nested, bulk seed detection linked to an interactive hierarchical tree interface. Alternatively, discontinuity analysis may be performed on the original 3D seismic data volume to identify boundaries between contiguous seismic objects.
The inventive method may be used to classify seismic objects according to a wide variety of morphologic parameters, including without limitation perimeter, area, volume, maximum thickness, minimum thickness, mean thickness, standard deviation of thickness, major and minor axes from principal component analyses, hierarchical medial axis skeleton analysis, 3D edge and surface curvature analysis, and time-conformable edge analysis.